Automatic road inventory using a low-cost mobile mapping system and based on a semantic segmentation deep learning model

Road maintenance is crucial for ensuring safety and government compliance, but manual measurement methods can be time-consuming and hazardous. This work proposes an automated approach for road inventory using a deep learning model and a 3D point cloud acquired by a low-cost mobile mapping system. The road inventory includes the road width, number of lanes, individual lane widths, superelevation, and safety barrier height. The results are compared with a ground truth on a 1.5 km subset of road, showing an overall intersection-over-union score of 84% for point cloud segmentation and centimetric errors for road inventory parameters. The number of lanes is correctly estimated in 81% of cases. This proposed method offers a safer and more automated approach to road inventory tasks and can be extended to more complex objects and rules for road maintenance and digitalization. The proposed approach has the potential to pave the way for building digital models from as-built infrastructure acquired by mobile mapping systems, making the road inventory process more efficient and accurate.Agencia Estatal de Investigación | Ref. RYC2021-033560-ICentro para el Desarrollo Tecnológico Industrial | Ref. IDI-2018111

Automated road extraction from terrestrial based mobile laser scanning system using the GVF snake model

Accurate extraction and reconstruction of route corridor features from geospatial data is a prerequisite to effective management of road networks for engineering, safety and environmental applications. High quality road geometry and road side features can now be extracted from dense point cloud LiDAR data, recorded by modern day Mobile Mapping Systems. This valuable route network information is gaining the attention of road safety and maintenance engineers. Road points are needed to be correctly identified, classified and extracted from LiDAR data before reconstructing intrinsic road geometry and road-side infrastructure. In this paper, we present a method to automatically extract the road from terrestrial based mobile laser scanning system using the GVF (Gradient Vector Flow) snake model. A snake is an energy minimizing spline that moves towards the desired feature or object boundary under the influence of internal forces within the curve itself and external GVF forces derived typically from 2D imaging data by minimizing certain energy such as edges or high frequency information. In our novel method, we initialise the snake contours over point cloud data based on the trajectory information produced by the MMS navigation sub-system. The internal energy term provided to the snake contour is based on adjusting the intrinsic properties of the curve, such as elasticity and bending, whilst the GVF energy and constraint energy terms are derived from the LiDAR point cloud attributes. Our method primarily differs from the traditional snake models in initialisation and in deriving the energy terms from the 3D LiDAR data

Road safety evaluation through automatic extraction of road horizontal alignments from Mobile LiDAR System and inductive reasoning based on a decision tree

13 p.Safe roads are a necessity for any society because of the high social costs of traffic accidents. This challenge is addressed by a novel methodology that allows us to evaluate road safety from Mobile LiDAR System data, taking advantage of the road alignment due to its influence on the accident rate. Automation is obtained through an inductive reasoning process based on a decision tree that provides a potential risk assessment. To achieve this, a 3D point cloud is classified by an iterative and incremental algorithm based on a 2.5D and 3D Delaunay triangulation, which apply different algorithms sequentially. Next, an automatic extraction process of road horizontal alignment parameters is developed to obtain geometric consistency indexes, based on a joint triple stability criterion. Likewise, this work aims to provide a powerful and effective preventive and/or predictive tool for road safety inspections. The proposed methodology was implemented on three stretches of Spanish roads, each with different traffic conditions that represent the most common road types. The developed methodology was successfully validated through as-built road projects, which were considered as “ground truth.”S

LiDAR-Based Mobile Mapping System for Lane Width Estimation in Work Zones

Lane width evaluation is one of the crucial aspects in road safety inspection. Moreover, lane width is one of the most important attributes of a road digital map for many applications, such as driver assistance systems and risk assessment. Using mobile mapping systems (MMSs) equipped with laser scanners is a safe and cost-effective method for rapidly collecting detailed information about various characteristics along any transportation corridor. This report presents an approach to derive lane width estimates using point clouds acquired from a calibrated mobile mapping system. To derive point clouds with high positional accuracy, estimation of the mounting parameters relating the different laser scanners to the onboard GNSS/INS unit is the foremost and necessary step. This report proposes a multi-unit LiDAR system calibration procedure where the mounting parameters can be estimated through minimizing the normal distance between conjugate planar/linear features in overlapping point clouds derived from different drive-runs. After generating the LiDAR point cloud using the estimated mounting parameters, the road surface can be extracted with the assistance of navigation data, which in turn is used to identify lane markings. Lane markings have a high retro-reflective property that will be exhibited as high-intensity points when scanned by a laser scanner. This property can be used to distinguish lane markings from the extracted road surface in the LiDAR point cloud. Then, non-lane marking points among the extracted high-intensity points are identified and removed. Next, the lane marking centerline is derived for lane width estimation

Hybrid-Vehfog: A Robust Approach for Reliable Dissemination of Critical Messages in Connected Vehicles

Vehicular Ad-hoc Networks (VANET) enable efficient communication between vehicles with the aim of improving road safety. However, the growing number of vehicles in dense regions and obstacle shadowing regions like Manhattan and other downtown areas leads to frequent disconnection problems resulting in disrupted radio wave propagation between vehicles. To address this issue and to transmit critical messages between vehicles and drones deployed from service vehicles to overcome road incidents and obstacles, we proposed a hybrid technique based on fog computing called Hybrid-Vehfog to disseminate messages in obstacle shadowing regions, and multi-hop technique to disseminate messages in non-obstacle shadowing regions. Our proposed algorithm dynamically adapts to changes in an environment and benefits in efficiency with robust drone deployment capability as needed. Performance of Hybrid-Vehfog is carried out in Network Simulator (NS-2) and Simulation of Urban Mobility (SUMO) simulators. The results showed that Hybrid-Vehfog outperformed Cloud-assisted Message Downlink Dissemination Scheme (CMDS), Cross-Layer Broadcast Protocol (CLBP), PEer-to-Peer protocol for Allocated REsource (PrEPARE), Fog-Named Data Networking (NDN) with mobility, and flooding schemes at all vehicle densities and simulation times

A Platform for Proactive, Risk-Based Slope Asset Management, Phase II

INE/AUTC 15.0

DFCV: A Novel Approach for Message Dissemination in Connected Vehicles using Dynamic Fog

Vehicular Ad-hoc Network (VANET) has emerged as a promising solution for enhancing road safety. Routing of messages in VANET is challenging due to packet delays arising from high mobility of vehicles, frequently changing topology, and high density of vehicles, leading to frequent route breakages and packet losses. Previous researchers have used either mobility in vehicular fog computing or cloud computing to solve the routing issue, but they suffer from large packet delays and frequent packet losses. We propose Dynamic Fog for Connected Vehicles (DFCV), a fog computing based scheme which dynamically creates, increments and destroys fog nodes depending on the communication needs. The novelty of DFCV lies in providing lower delays and guaranteed message delivery at high vehicular densities. Simulations were conducted using hybrid simulation consisting of ns-2, SUMO, and Cloudsim. Results show that DFCV ensures efficient resource utilization, lower packet delays and losses at high vehicle densities